The propagation of sound in a medium follows diffraction theory. Proper focusing, under the Fresnel approximation, can achieve diffraction-limited resolution, where the field pattern is given by the spatial Fourier transform of the aperture function.
This inherent nature of ultrasound imaging gives rise to side lobes (or clutter). These effects are undesirable side effects since they degrade image quality by lowering the contrast to noise ratio (“CNR” or contrast resolution) and the detectability of small targets (or spatial resolution). Typically a type of Gaussian windowing function such as Hanning or Hamming apodization is applied. The point spread function of a uniform aperture gives a narrower main beam or better spatial resolution with higher side lobe level compared to a Hanning or Hamming apodized aperture. These types of weighting functions are called linear apodization functions since the same weighting is applied across the aperture independent of depth or of imaging line. They lower the side lobes at the expense of the main lobe resolution. Therefore there have been numerous researches in nonlinear side lobe suppression methods, which aim for little or no loss main lobe resolution.
The goal of beamforming is to focus ultrasound energy to one location only, but this is not truly achievable with standard delay and sum beamforming. This gives rise to off-axis sidelobes and clutter. These sidelobes or clutter inherent in ultrasound imaging are undesirable side effects since they degrade image quality by lowering CNR and the detectability of small targets.
As described previously, one way to improve CNR is to reduce sidelobe and clutter levels by applying a weighting or shaping function such as a Hanning or Hamming apodization across the transmit and receive apertures. These types of weighting functions are called linear apodization functions since the same weighting is applied to the aperture independent of depth or of imaging line. As a trade-off, they lower the sidelobes at the expense of worse mainlobe lateral resolution. To avoid making this trade-off, there have been several publications in nonlinear sidelobe suppression methods which aim for little or no loss in mainlobe resolution while achieving low clutter levels commonly associated with apodization.
In recent work, Guenther and Walker developed optimal apodization functions using constrained least squares theory. This method creates apodization functions with the goal of limiting the energy of the point spread function (PSF) outside a certain area and maintaining a peak at the focus. A point target simulation was performed using a linear array with 192 elements with 200 μm element pitch and a transmit frequency of 6.5 MHz. Using this method, a 5-10 dB reduction in sidelobe levels compared to a Hamming apodization was achieved. Wang used a comparator to select the minimum magnitude from two or more sets of data using various apodization methods, such as uniform, Hanning or Hamming. By taking the minimum magnitude on a pixel-by-pixel basis, this method preserves the mainlobe resolution of the uniformly apodized data and lowers sidelobes similar to a Hanning or Hamming apodized data. Stankwitz developed a spatially variant nonlinear apodization (SVA) technique, which uses the lateral phase differences between Hanning and uniformly apodized data to distinguish between mainlobe and clutter signals. This is accomplished by taking advantage of the properties of raised-cosine weighting functions and finding the optimal apodization function on a pixel-by-pixel basis.
H. C. Stankwitz explored spatially variant nonlinear apodization (known as SVA) technique, which uses the phase differences between Hanning and uniformly apodized data. The optimal apodization is achieved on a pixel-by-pixel basis. Hong Wang used the idea of Stankwitz where a comparator is used to select a minimum amplitude from two more sets of data using various apodization methods, such as uniform, Hanning or Hamming. By using the minimum amplitude on a pixel-by-pixel basis, this method preserves the main lobe resolution of the uniformly apodized data and lowers side lobes similar to a Hanning or Hamming apodized data. Pai-Chi Li expanded the idea of coherence factor (CF) to get generalized coherence factor (GCF) to calculate the spectral energy ratio. He demonstrated that the low frequency component of the element domain spectrum corresponds to the coherent portion of the received data, and that the high frequency component corresponds to the incoherent portion. The coherence factor matrix is calculated as the ratio of the spectral energy within a low frequency region to the total energy and used as a pixel-by-pixel “weighting” matrix.
Another well known example is parallel adaptive receive compensation algorithm (PARCA). Using total least square (TLS), this method works well with a point target but the improvement is disputable with speckled targets. A modified version PARCA2 also was proposed where the parallel beam formation is approximated by Fourier transform of the aperture data and an iterative scheme is used to simplify the calculation in PARCA.
All these methods are successful in lowering side lobe level. The results, however, are obtained at certain imaging conditions or at the expense of heavy computation and extra hardware circuits.
Even though ultrasound has been used to image the human body for at least 50 years and is one of the most widely used diagnostic tools in modern medicine, the inherent nature of ultrasound imaging gives rise to side lobes (or clutter) which are undesirable side effects since they degrade image quality by lowering the contrast to noise ratio (or contrast resolution) and the detectability of small targets (or spatial resolution).
An ideal contrast improvement method would greatly improve contrast such that lesions are easily visualized without significantly increasing computational complexity, worsening lateral and/or temporal resolution.